Preoperative pain hypersensitivity is associated with axial pain after posterior cervical spinal surgeries in degenerative cervical myelopathy patients: a preliminary resting-state fMRI study

Objective To test whether preoperative pain sensitivity is associated with the postoperative axial pain (PAP) in degenerative cervical myelopathy (DCM) and to explore its underlying brain mechanism. Methods Clinical data and resting-state fMRI data of 62 DCM patients along with 60 age/gender matched healthy participants were collected and analysed. Voxel-wise amplitude of low frequency fluctuation (ALFF) was computed and compared between DCM patients and healthy controls. Correlation analyses were performed to reveal the association between the clinical metrics and brain alterations. Clinical data and ALFF were also compared between DCM patients with PAP and without PAP. Results (1) Relative to healthy participants, DCM patients exhibited significantly lower preoperative pain threshold which is associated with the PAP intensity; (2) Relative to patients without PAP, PAP patients exhibited increased ALFF in mid-cingulate cortex (MCC) and lower preoperative pain threshold; (3) Further, multivariate pattern analysis revealed that MCC ALFF provide additional value for PAP vs. non-PAP classification. Conclusion In conclusion, our findings suggest that preoperative pain hypersensitivity may be associated with postoperative axial pain in degenerative cervical myelopathy patients. This finding may inspire new therapeutic ideas for patients with preoperative axial pain. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-022-01332-2.

Patients with Degenerative Cervical Myelopathy (DCM) were recruited according to the following criteria: (1) Evidence of myelopathy along the cervical spine (C3-C7) on cervical MRI; (2) Signs and symptoms of myelopathy correspond to MRI findings (e.g., sensorimotor deficits, bladder/bowel dysfunction, gait disturbance, etc.); (3) No prior history of cervical spinal surgery and agree to undergo decompression surgery (e.g., laminoplasty); (4) able to complete fMRI scan; and (5) no stenosis of the extracranial vertebral artery or the carotid artery following Doppler ultrasound examination: (6) No indication of any other neurological, psychiatric, ocular, or systemic diseases, including hypertension and diabetes; and (7) no history of alcohol or substance abuse.
The following criteria were used to recruit healthy subjects of similar age, gender, and education through advertisements (1) no evidence of spinal compression; (2) no other spinal or brain neurological disorders, or systemic disease; and (3) ability to complete fMRI studies; (4) No indication of any other neurological, psychiatric, ocular, or systemic diseases, including hypertension and diabetes; and (5) no history of alcohol or substance abuse.

Data acquisition
3T fMRI data were acquired using a MAGNETOM Prisma 3T MR scanner (Siemens, Erlangen, Germany) with a 64-channel phase-array head-neck coil. Sponge pads were performed to all participants to support the head for minimizing the head movement during the scan. All participants were clearly instructed to keep their eyes Insights Imaging (2022) Su Q, Li J, Chu X, Zhao R closed and remain awake, while avoiding specific and strong thoughts. Furthermore, the head motion of the functional scan of each participant were calculated and the participants whose head motion were not within defined motion thresholds (i.e., translational or rotational motion parameters less than 2 mm or 2°) were required to underwent the fMRI scan again to minimize the effect of head motion.

Data preprocessing
Functional MR data were preprocessed using the Data Processing Assistant for rs-fMRI (DPARSF; http://www.restfmri.net/forum/DPARSF) toolbox. The detailed preprocessing procedures were as following: (1) The first 10 volumes of each functional scan were excluded due to the acclimatization to the scanning environment and magnetization stabilization; (2) Motion correction were performed to remove the effect of head movement; (3) Functional images were co-registered to structural images and spatially normalized to the Montreal Neurological Institute template and each voxel was resampled to 3×3×3mm3; (4) The liner-drift, Friston-24 parameters, the mean global signal, the white matter signal, and CSF signal were extracted as covariates and regressed out to minimize nonneural signals; (5) Subsequently, Insights Imaging (2022) Su Q, Li J, Chu X, Zhao R scrubbing for high motion timepoints was also performed; (6) Finally, a bandpass filter (0.01 ~ 0.08 Hz) was then applied to remove high-frequency noise effects; (7) resultant functional images were smoothed with an 8 mm full-width-half-maximum isotropic Gaussian kernel.

Leave-one-out-cross-validation procedure
In LOOCV: First, one data-point in the available dataset was held-out. Features were used to train a support vector machine model within the rest of the dataset and the model was then tested using the held-out test data-point, thereby yielding a predicted label for the test data-point. This procedure was repeated until each datapoint was held out once as the test data-point. After that, an accuracy for this classification, which was determined as the proportion of accurate predictions out of total predictions were made, was used to evaluate the performance of the SVM model.
The corresponding P-value was derived from the null distribution that was obtained from 1000 random permutation tests, by randomly shuffling the labels of the subjects in the training dataset, with the corresponding feature set. Specifically, the P-values were determined as a proportion of the number of permutations greater than or equal to the actual classification accuracy out of the total permutations. If none of the 1000 permutations reached the actual classification accuracy, the p-value was considered to be P < 0. 001.

Permutation test
we used a permutation test method as following: (1) the difference between these two classification accuracies was calculated; (2) the labels of the subjects were randomly shuffled and divided into two groups. Subsequently, classification analyses were performed via SVM using clinical metrics combined with ALFF and just clinical metrics as features respectively; (3) the difference of the two classification accuracies obtained from step 2 was them calculated. These procedures were repeated 1000 times to obtain a null distribution and P-value as determined as a proportion of the